TomoPy : a framework for the analysis of synchrotron tomographic data
Analysis of tomographic datasets at synchrotron light sources (including X-ray transmission tomography, X-ray fluorescence microscopy and X-ray diffraction tomography) is becoming progressively more challenging due to the increasing data acquisition rates that new technologies in X-ray sources and d...
| Publié dans: | Journal of synchrotron radiation. - 1994. - 21(2014), Pt 5 vom: 22. Sept., Seite 1188-93 |
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| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2014
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| Accès à la collection: | Journal of synchrotron radiation |
| Sujets: | Journal Article Research Support, U.S. Gov't, Non-P.H.S. X-ray imaging phase retrieval tomography |
| Résumé: | Analysis of tomographic datasets at synchrotron light sources (including X-ray transmission tomography, X-ray fluorescence microscopy and X-ray diffraction tomography) is becoming progressively more challenging due to the increasing data acquisition rates that new technologies in X-ray sources and detectors enable. The next generation of synchrotron facilities that are currently under design or construction throughout the world will provide diffraction-limited X-ray sources and are expected to boost the current data rates by several orders of magnitude, stressing the need for the development and integration of efficient analysis tools. Here an attempt to provide a collaborative framework for the analysis of synchrotron tomographic data that has the potential to unify the effort of different facilities and beamlines performing similar tasks is described in detail. The proposed Python-based framework is open-source, platform- and data-format-independent, has multiprocessing capability and supports procedural programming that many researchers prefer. This collaborative platform could affect all major synchrotron facilities where new effort is now dedicated to developing new tools that can be deployed at the facility for real-time processing, as well as distributed to users for off-site data processing |
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| Description: | Date Completed 30.03.2015 Date Revised 21.10.2021 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1600-5775 |
| DOI: | 10.1107/S1600577514013939 |